library(raster)                               ##Load the Raster Library
usa<-getData('GADM', country='USA', level=1)  ##Get the Province Shapefile for the US
plot(usa)                                     ##Plot this shapefile

bool <- (coordinates(usa)[,1] > -130) & (coordinates(usa)[,1] < -60)
usa<-usa[bool,]
plot(usa)

library(sp)
tor.sp <- tor ##Create new data frame

#promote an ordinary data frame
coordinates(tor.sp) = ~elon + elat       ##Identify field names that contain lat/lon
proj4string(tor.sp) <- proj4string(usa)  ##Assign projection

#plot on shapefile
plot(usa,col="lightgray")       ##plot shapefile
points(tor.sp,col="red")        ##plot points (do not need x,y...it is a spatial obj)

heatMap(tor.sp,usa,col="red",main="Tornado Data Denstiy")

myMap <- get_map(location = "kansas city", zoom = 5)    ##Get the map
myMap <- ggmap(myMap, extent = "device")                 ##Prepare Map

myMap +
  stat_density2d(aes(x = elon, y = elat, fill = ..level..,alpha=..level..),      ##Density
    bins = 10, geom = "polygon", data = tor) +
  scale_fill_gradient(low = "black", high = "red")+                              ##Gradient
  ggtitle("Map of US Tornado Density")                                           ##Title

library(ggmap)
APGMap<-qmap('6008 Jayhawk rd, aberdeen proving grounds, md', 
     source='google',
     maptype='terrain',
     zoom=10)

kansas<-qmap('fort leavenworth, ks', zoom=7)       ##Get Map for Kansas
kansas+                                            ##This plots the Kansas Map
geom_point(aes(x = elon, y = elat), data = tor)    ##This adds the points to it

kansas+                                                                ##Plot Kansas Map
  geom_point(aes(x = elon, y = elat, colour = f,size=wid),data = tor)+ ##Plot points
  scale_colour_gradient(low="blue",high="red")                         ##Color Gradient

aus.density<-pointdensity(aus.subset,lat_col="lat",lon_col="lon",grid_size=20,radius=50)
map_base <- qmap('Australia', zoom = 4, darken=0.2) 
map_base + geom_point(aes(x = lon, y = lat, colour = count,alpha=count), 
    shape = 16, size = 2, data = aus.density) + 
    scale_colour_gradient(low = "yellow", high = "red") 

leaflet(data=sydney) %>% 
  addTiles() %>% addMarkers(~lon, ~lat
)

photographers<-unique(sydney$V2)              #ID photographers
final<-list()                                 #Create OJB to store results
for(i in 1:length(photographers)){            
temp<-subset(sydney,V2==photographers[i])     #Subset by photographer
temp<-temp[order(temp$V4),]                   #Order by time
breakpoints <- c(FALSE,diff(temp$V4)>8*60)    #Establish break points if diff > 8min
temp$label <- as.numeric(cut.POSIXt(temp$V4, c(min(temp$V4), 
              temp[breakpoints, "V4"], max(temp$V4)+1)))    #Convert factor to label
temp$label<-paste(temp$V2,temp$label,sep="")  #Concatenate label with username
final[[i]]<-temp                              #Store temp data
}
final<-do.call("rbind", final)                #Convert from list to dataframe

library(pointdensityP)
H_tor <- pointdensity(df = tor, lat_col = "elat", lon_col = "elon", 
grid_size = 50, radius = 100)
map_base <- qmap(location="st louis, mo", source='stamen',maptype="toner",zoom = 5, darken=0.3)
map_base + 
  geom_point(aes(x = lon, y = lat, colour = count,alpha=count), 
    shape = 16, size = 2, data = H_tor) + 
  scale_colour_gradient(low = "yellow", high = "red")

library(TeachingDemos)
afg<-getData('GADM', country='AFG', level=1)  ##Get the Afghanistan Shapefile
wits_sp<-read.csv("wits_sp.csv",as.is=TRUE)   ##Load the WITS data from CSV

##Create the SpatialPointsDataFrame
coordinates(wits_sp)<-c("lon","lat")
proj4string(wits_sp)<-proj4string(afg)

##Now all you have to do is plot it!
plotPiePoly(data = wits_sp, shape=afg, factor = "flag", size = c(0.5, 0.5), legend = TRUE,
            main = "Sample Pieplot Over Polygon (WITS Data)")

players$lon <- NA;  players$lat <- NA
for(i in 1:nrow(players)){
    try(temp<-geocode(players$City[i]))
    players$lat[i]<-temp$lat
    players$lon[i]<-temp$lon
}

state<-usa[grep(myState,usa$VARNAME_1),]
plot(state)
with(players,points(lon,lat,pch=16,col="blue",cex=3*count/max(count)))